CN112698392A - Multilayer system reservoir three-dimensional drawing and oil-gas spatial distribution and constant volume method - Google Patents
Multilayer system reservoir three-dimensional drawing and oil-gas spatial distribution and constant volume method Download PDFInfo
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Abstract
The invention discloses a multilayer system reservoir three-dimensional depicting and oil-gas spatial distribution and constant volume method, which comprises the following steps: acquiring a three-dimensional seismic amplitude data volume and drilling logging data of a target area; carrying out construction guiding filtering processing on the three-dimensional seismic amplitude data volume; identifying fractures and cracks through a machine learning technology on the basis of the three-dimensional seismic amplitude data volume; solving an amplitude curvature attribute of the three-dimensional seismic amplitude data volume to predict a hole type reservoir stratum; performing well-seismic calibration on the three-dimensional seismic amplitude data volume, extracting wavelets, solving a wave impedance data volume, further calculating residual wave impedance, and identifying the cavern type reservoir by using the residual wave impedance data volume; and setting a prediction threshold value of each attribute under the quality control and constraint of drilling and logging data by combining the obtained identification prediction results of the fracture, the hole type reservoir and the hole type reservoir, so as to realize the three-dimensional depiction of the multilayer system reservoir and the oil-gas spatial distribution and constant volume calculation.
Description
Technical Field
The invention relates to the technical field of oil-gas exploration, in particular to a multilayer system reservoir three-dimensional depiction and oil-gas spatial distribution and constant volume method.
Background
The internal development of the sliding fracture zone is divided into three types, namely a cave, a hole and a crack, the three types of reservoirs have obvious differences in seismic reflection characteristics and attribute characteristics, prediction is carried out by classification through different technical means, and finally fusion treatment is carried out, so that the aim of comprehensively evaluating the reservoirs in the fracture zone is achieved.
At present, researchers develop a series of research works aiming at the identification and description of the fractured-solvent oil-gas reservoir, because the fractured-solvent oil-gas reservoir has large buried depth and weakened seismic reflection characteristics, the conventional technical means is difficult to fully excavate seismic data information, and the space depiction effect of a fracture zone is influenced, which is mainly shown in that the identification modes of different reservoir types such as cracks, holes and caves are difficult to determine, the geophysical identification precision of the fractured-solvent reservoir is low, different types of fractured-solvent reservoirs cannot be effectively distinguished, the space distribution characteristics and the connectivity of the fractured-solvent are difficult to determine, and the quantitative description is difficult.
Aiming at the technical difficulties in the previous research, the development characteristics of different types of dissolved-fluid reservoirs are described finely by applying various advanced geophysical means such as machine learning fracture identification, amplitude curvature attribute, compressive sensing inversion and the like, and a sufficient basis is provided for space depiction and constant volume measurement of the dissolved-fluid reservoirs under the control of deep fracture.
Disclosure of Invention
In order to solve the technical problems, the invention provides a multilayer system reservoir body three-dimensional drawing and oil-gas spatial distribution and constant volume.
In order to achieve the purpose, the invention adopts the following technical scheme:
a multilayer system reservoir body three-dimensional drawing and oil-gas space distribution and constant volume method comprises the following steps:
s100, acquiring a three-dimensional seismic amplitude data volume and drilling logging data of a target area;
s200, conducting construction guiding filtering processing on the three-dimensional seismic amplitude data volume;
s300, identifying fractures and cracks through a machine learning technology on the basis of the three-dimensional seismic amplitude data volume;
s400, solving an amplitude curvature attribute of the three-dimensional seismic amplitude data volume to predict a hole type reservoir stratum;
s500, performing well-seismic calibration on the three-dimensional seismic amplitude data volume, extracting wavelets, solving a wave impedance data volume, further calculating residual wave impedance, and identifying a cavern type reservoir by using the residual wave impedance data volume;
s600, combining the obtained identification and prediction results of the fracture, the hole type reservoir and the cave type reservoir, setting prediction threshold values of machine learning fracture and crack attributes, amplitude curvature attributes and inversion residual wave impedance attributes under the quality control and constraint of drilling and logging data, and achieving three-dimensional depiction of the multilayer system reservoir body, oil-gas spatial distribution and constant volume calculation.
The following is described for each step:
s100, obtaining a three-dimensional seismic amplitude data body and drilling logging data of a target area.
When the segmented identification and evaluation method is implemented, a three-dimensional seismic amplitude data volume of a target area needs to be obtained firstly, and at least 3-5 wells have acoustic wave and density well logging curves along a sliding fracture zone to be used as input of compressed sensing inversion.
And S200, performing construction guiding filtering processing on the three-dimensional seismic amplitude data volume.
Background noise often exists in pure wave superposition data or migration data after seismic processing, and fault and crack identification is interfered, so that targeted filtering is needed to improve the signal-to-noise ratio of seismic data and enhance the definition of faults and cracks. The main idea of the structure-oriented filtering processing is to filter along the structure direction obtained by the seismic amplitude data, effectively improve the signal-to-noise ratio of seismic data and achieve high-definition imaging of faults and cracks on the basis of discontinuity detection result constraints such as coherent bodies.
According to the multilayer system reservoir body three-dimensional depiction and oil-gas spatial distribution and volume fixing method, preferably, the step of performing structure-oriented filtering processing on the three-dimensional seismic amplitude data body in the step S200 comprises the following steps:
s201, determining the azimuth of a reflection homophase axis through an earthquake inclination angle and an azimuth angle;
s202, calculating a coherent body, and determining the positions of main faults and cracks;
and S203, expanding filtering under the constraints of the inclination angle, the azimuth angle and the coherent body to finish the construction guiding filtering processing.
According to the multilayer system reservoir three-dimensional depicting and oil-gas spatial distribution and constant volume method, preferably, the filtering process specifically comprises the following steps: carrying out discontinuity detection according to the coherent body, and confirming a fracture development position; seismic data are not smoothed for identified fracture development locations, otherwise smoothed along the formation.
And S300, identifying the fracture and the crack through a machine learning technology on the basis of the three-dimensional seismic amplitude data volume.
According to the multilayer system reservoir three-dimensional depicting and oil-gas spatial distribution and constant volume method, preferably, the machine learning technology is a convolutional neural network machine learning technology.
According to the multilayer system reservoir three-dimensional depicting and oil-gas spatial distribution and constant volume method, preferably, the convolutional neural network machine learning technology specifically adopts a fracture detection algorithm based on CNN image segmentation.
With the rapid development of machine learning technology, Convolutional Neural Network (CNN) technology has been widely used in seismic reservoir prediction. Theoretically, the technology is very suitable for automatic tracking of fracture and cracks. The fracture detection algorithm based on CNN image segmentation converts the image classification problem into the image segmentation problem, and realizes high-precision fracture identification by using a CNN network based on Unet, and has the following main characteristics and advantages:
firstly, training data can be interactively picked up in a three-dimensional space, and various possible conditions capable of simulating fracture development can be generated through a random model, so that full-automatic supervised neural network learning based on big data is realized;
secondly, the most advanced Unet CNN network is used for solving the image segmentation problem;
and thirdly, the GPU is used for solving the problem of a large amount of operation.
S400, solving an amplitude curvature attribute of the three-dimensional seismic amplitude data volume to predict the hole type reservoir stratum.
The beneficial reservoirs of carbonate fracture-cave systems are generally divided into three types: fractured reservoir, caverned reservoir. In the envelope surface of the fracture zone, fracture development is closely related to fault and hole development, and areas with developed general fractures and holes are often accompanied by more dense fractures, so that fractured reservoirs generally show disordered reflection in earthquake and often show abnormal attributes related to fractures and holes in earthquake attributes. The method has the advantages that the forward modeling attribute simulation shows that the amplitude curvature has a good identification effect on the hole type reservoir, and the hole type reservoir can be effectively identified through the amplitude curvature attribute, so that the method adopts the amplitude curvature attribute to predict the hole type reservoir.
The amplitude curvature is derived from the seismic data amplitude by a second order transverse derivation. Firstly, the first derivative of the main survey line direction and the cross survey line direction is calculated by using the seismic amplitude or energy, and the obtained energy gradient attribute can reflect the abnormal geologic body, which is generally called amplitude energy gradient. And then carrying out second-order derivation on the amplitude curved surface to obtain an amplitude curved surface, and finally calculating each amplitude curvature attribute according to the surface fitting. In principle, the amplitude energy gradient is a manifestation of the edges of the geologic body, and therefore the spatial distribution of the geologic body cannot be obtained by the threshold value. The amplitude curvature converts the amplitude energy gradient into an attribute reflecting the geologic body envelope, and the geologic body spatial distribution can be obtained through a threshold value.
According to the multilayer system reservoir body three-dimensional depiction and oil-gas space distribution and constant volume method, preferably, the amplitude curvature attribute is obtained by performing transverse second-order derivation on seismic data amplitude; firstly, the first derivative of the direction of a main survey line and the direction of an interconnection survey line is calculated by utilizing seismic amplitude or energy to obtain an amplitude energy gradient, then, the second derivative is carried out to obtain an amplitude curved surface, and finally, the curvature attribute of each amplitude is calculated according to the surface fitting.
S500, well-seismic calibration is carried out on the three-dimensional seismic amplitude data volume, wavelets are extracted, a wave impedance data volume is obtained, residual wave impedance is further calculated, and the cave-type reservoir is identified by utilizing the residual wave impedance data volume.
The identification of the cave-type reservoir is a difficulty in the identification of the reservoir of a carbonate rock slotted hole system, and due to the limitation of seismic resolution, the position and the size of the cave-type reservoir are difficult to accurately position through conventional seismic attribute research, so that the identification of the cave-type reservoir needs to be carried out by adopting a high-resolution inversion means. The results of petrophysical research show that the impedance difference is still an effective distinguishing parameter between carbonate reservoirs and non-reservoirs, and the lower the impedance generally indicates that the reservoir physical properties are better. The cave-type reservoir is filled with mud and fluid and has obvious low impedance characteristics, so that the cave-type reservoir can be identified by adopting an inversion method.
According to the multilayer system reservoir body three-dimensional depiction and oil-gas spatial distribution and volume fixing method, preferably, the wave impedance data body is obtained through compressive sensing inversion in S500.
According to the method, a compressive sensing high-resolution inversion method is adopted for identifying the cavernous reservoir, a compressive sensing algorithm solves the sparse inversion problem through an L1 norm (conventional inversion uses an L2 norm), and the stratum reflection coefficient is assumed to be characterized through odd-even pole decomposition. The algorithm realizes that the precision and the resolution of an inversion result are improved by introducing the wavelet matrix into the deconvolution of the wedge-shaped dictionary. The objective function of the inversion is:
Min[||s-Wr||2+λ||r||p]
where s is seismic data, W is a wavelet, r is a reflection coefficient, λ is a regularization coefficient, and p is a norm. In compressed perceptual inversion p is 1.
The compressed sensing inversion uses a compressed sensing theory in the algorithm implementation process, and the obtained wave impedance is higher than the deterministic inversion resolution of the traditional commercial software; meanwhile, the noise immunity and stability are better, and the elastic parameters obtained by inversion are more accurate.
According to the multilayer system reservoir body three-dimensional depiction and oil-gas spatial distribution and volume-fixing method, preferably, the calculation process of the residual wave impedance comprises the following steps: and carrying out median filtering on the wave impedance data volume to obtain a relatively smooth wave impedance data volume, and then subtracting the original wave impedance data volume to obtain the residual wave impedance.
The transverse heterogeneity of the solution reservoir is very strong, the obtained wave impedance inversion section has certain continuity in the transverse direction, and the heterogeneity characteristic is not obvious enough, which is also determined by the characteristics of seismic data. In order to highlight the heterogeneity of a solution reservoir and eliminate the influence of transverse continuous seismic reflection, residual wave impedance processing is carried out on a wave impedance inversion result, and the processing method is that median filtering is carried out on the wave impedance inversion result to obtain a relatively smooth wave impedance data volume, and then the relatively smooth wave impedance data volume is subtracted from the original wave impedance data volume to obtain residual wave impedance.
S600, combining the obtained identification and prediction results of the fracture, the hole type reservoir and the cave type reservoir, setting prediction threshold values of machine learning fracture and crack attributes, amplitude curvature attributes and inversion residual wave impedance attributes under the quality control and constraint of drilling and logging data, and achieving three-dimensional depiction of the multilayer system reservoir body, oil-gas spatial distribution and constant volume calculation.
The fracture-cavity carbonate rock aggregate has a complex internal structure and strong heterogeneity, cracks, holes and cavities are developed, and fracture-cavity development zones and cavity boundaries can be identified through multi-attribute fusion identification. In the process of attribute fusion, firstly, threshold values of different attributes are determined, and the data such as a drilling time curve, an emptying loss point and the like are mainly referred. Statistics show that the time-of-drilling curve is sensitive to the broken solution boundary and becomes faster when the broken solution is drilled, so that the time-of-drilling curve can be used to determine the threshold value of the attribute. When the drill encounters a fracture, a hole, a cave or a dense zone of cracks, loss, emptying and the like generally occur, so that threshold values of different attributes can be determined by utilizing the information.
According to the multilayer system reservoir three-dimensional depiction and oil-gas spatial distribution and constant volume method, prediction threshold values of different attributes are preferably determined by referring to a drilling time curve and emptying loss point data.
Due to the fact that the porosity of reservoirs of different types has large difference, the porosity of reservoirs of different types needs to be classified and evaluated, effective volumes of reservoirs of different types are obtained, and therefore the trap resource amount is estimated.
The effective capacity calculation of fracture-cavity carbonate reservoirs is divided into three types, namely fractures, holes and caves, the physical property difference of different types of reservoirs is large, and the volume estimation needs to be carried out by adopting different methods.
According to the multilayer system reservoir body three-dimensional depiction and oil-gas space distribution and constant volume method, preferably, the constant volume calculation comprises the following steps:
1) a fractured reservoir: logging and explaining the porosity range of the reservoir, taking the average value of the porosity range as the porosity of the fractured reservoir, identifying fracture and fracture by combining a machine learning technology, and calculating the effective volume of the fractured reservoir;
2) a pore type reservoir layer: logging and explaining the porosity range of the reservoir, and taking the average value of the porosity range as the porosity of the pore type reservoir; identifying the envelope surface of the hole type reservoir through the amplitude curvature, and determining the total volume of the space of the reservoir so as to obtain the effective volume of the hole type reservoir;
3) cave-type reservoir bed: estimating the porosity of the cave-type reservoir by using a wave impedance data volume obtained by compressed sensing inversion, and obtaining the effective volume of the cave-type reservoir through the porosity;
4) and adding the effective volumes of the three types of reservoirs to obtain the effective volume of the multi-series reservoir.
The multilayer system reservoir three-dimensional depicting and oil-gas spatial distribution and constant volume method combines various seismic attributes, realizes accurate comprehensive depicting and constant volume calculation of the carbonate rock fracture-cave system under well control constraint, and practical project research verifies the reliability of the method, so that the method can effectively identify the three-dimensional spatial distribution and quantitative evaluation of the carbonate rock fracture-cave system, and has wide popularization value.
Drawings
FIG. 1a is a flow chart of the operation of the guided filtering process in the embodiment.
FIG. 1b is one of the comparison graphs of the effect of the structured guided filtering process in the embodiment.
FIG. 1c is a second comparison graph of the effect of the constructed guided filtering process in the embodiment.
Fig. 2a is a structure diagram of CNN network based on the pnet in the present invention.
FIG. 2b is a cross section of the recognition result of the machine learning fracture in the embodiment.
FIG. 2c is a plan view of the machine-learned fracture identification result of the embodiment along the top boundary slice of the Ordovician suite.
FIG. 2d is a plan view of the machine-learned fragmentation recognition result along the bottom boundary slice of the Midamid system in the example.
FIG. 3a is a superimposed graph of fracture and amplitude curvature effects on hole type reservoir identification profiles in the examples.
FIG. 3b is a superimposed graph of the fracture and amplitude curvature effect on the plane of pore type reservoir identification in the examples.
FIG. 4a is a seismic model of linear fractures + a single cavern in an example.
FIG. 4b is a seismic section of the example linear fracture + single cavern.
FIG. 4c is a wave impedance inversion cross-section of the linear fracture + single cavern in the example.
FIG. 4d is a section view of the compressive sensing wave impedance inversion of the linear fracture + single cavern in the example.
FIG. 4e is a seismic model of linear fracturing + fracture + dual caverns in an example.
FIG. 4f is a seismic section of the example of a linear fracture + dual cavern.
FIG. 4g is a wave impedance inversion cross section of the linear fracture + double cavern in the example.
FIG. 4h is a section view of the compressive sensing wave impedance inversion of the linear fracture + double cavern in the embodiment.
FIG. 4i is a compressed sensing inversion residual impedance profile of an embodiment.
FIG. 5a is a diagram illustrating the fusion effect of the solution-breaking property in the example.
FIG. 5b is a carving view of the fractured-solution internal fractured reservoir volume in the example.
FIG. 5c is a diagram illustrating the carving of the reservoir volume of the internal cavity type of the solution breaker in the example.
FIG. 5d is a statistical plot of intersection of wave impedance and well logging porosity for cavernous reservoir inversion in an example.
FIG. 5e1 is an example cavern reservoir seismic section.
FIG. 5e2 is the wave impedance inversion profile of the cavernous reservoir in the example.
Fig. 5e3 is the cavernous reservoir porosity profile of the example.
FIG. 5f is a cave-type reservoir volume engraving diagram inside the solution breaking body in the embodiment.
FIG. 5g is a graph showing the effective volume of the broken solution in the embodiment of the broken zone gauge.
Detailed Description
In order to more clearly illustrate the invention, the invention is further described below in connection with preferred embodiments. It is to be understood by persons skilled in the art that the following detailed description is illustrative and not restrictive, and is not to be taken as limiting the scope of the invention.
The embodiment of the invention adopts a preferred scheme to carry out multilayer system reservoir three-dimensional drawing and oil-gas spatial distribution and constant volume on a certain target area, and comprises the following steps:
s100, obtaining a three-dimensional seismic amplitude data body and drilling logging data of a target area.
When the segmented identification and evaluation method is implemented, a three-dimensional seismic amplitude data volume of a target area needs to be obtained firstly, and at least 3-5 wells have acoustic wave and density well logging curves along a sliding fracture zone to be used as input of compressed sensing inversion.
And S200, performing construction guiding filtering processing on the three-dimensional seismic amplitude data volume.
Background noise often exists in pure wave superposition data or migration data after seismic processing, and fault and crack identification is interfered, so that targeted filtering is needed to improve the signal-to-noise ratio of seismic data and enhance the definition of faults and cracks. The main idea of the structure-oriented filtering processing is to filter along the structure direction obtained by the seismic amplitude data, effectively improve the signal-to-noise ratio of seismic data and achieve high-definition imaging of faults and cracks on the basis of discontinuity detection result constraints such as coherent bodies. The technique comprises three main steps:
s201, determining the azimuth of a reflection homophase axis through an earthquake inclination angle and an azimuth angle;
s202, calculating a coherent body, and determining the positions of main faults and cracks;
and S203, expanding filtering under the constraints of the inclination angle, the azimuth angle and the coherent body to finish the construction guiding filtering processing.
The operation flow chart is as shown in fig. 1a, the dip angle and the azimuth angle of the seismic data are calculated, the coherent body is calculated to carry out discontinuity detection, whether strong discontinuity exists in the time window or not is analyzed according to the coherent body calculation result, if the discontinuity is strong, the discontinuity is considered to be a fracture development position, the seismic data are not smoothed, otherwise, the discontinuity is carried out along the structure, and the number of transverse channels and the longitudinal time window are adjusted in the calculation process to obtain an ideal result. This process may be iterated until the best result is obtained.
As shown in FIG. 1b and FIG. 1c, from the comparison of the fault enhancement effect of the seismic data, the signal-to-noise ratio of the processed seismic section is obviously improved, the longitudinal continuity of the fault is obviously enhanced, the fault interpretation multi-solution is obviously reduced, and the high-quality seismic data are provided for the identification of the walk-slip fault.
And S300, identifying the fracture and the crack through a convolutional neural network machine learning technology on the basis of the three-dimensional seismic amplitude data volume.
With the rapid development of machine learning technology, Convolutional Neural Network (CNN) technology has been widely used in seismic reservoir prediction. Theoretically, the technology is very suitable for automatic tracking of fracture and cracks. The fracture detection algorithm based on CNN image segmentation converts the image classification problem into the image segmentation problem, and realizes high-precision fracture identification by using a CNN network based on Unet, and has the following main characteristics and advantages:
firstly, training data can be interactively picked up in a three-dimensional space, and various possible conditions capable of simulating fracture development can be generated through a random model, so that full-automatic supervised neural network learning based on big data is realized;
secondly, the most advanced Unet CNN network is used for solving the image segmentation problem;
and thirdly, the GPU is used for solving the problem of a large amount of operation.
Fig. 2a is a CNN network structure fracture identification schematic diagram based on the Unet, and the algorithm has high requirements on basic training data and needs to pick up a seed line or a big data fracture model established in the early stage with high precision as a support. The calculation process mainly comprises the following two steps: the method comprises the steps of interactively picking up the fracture development condition of seed lines in a three-dimensional space, and generating various possible conditions capable of simulating the fracture development through a random model, so that the full-automatic supervised neural network learning based on big data is realized.
The machine learning technology is adopted to identify and apply sliding fracture and crack of the Tarim basin, a machine learning fracture identification result section is shown in fig. 2b, a machine learning fracture identification result section is shown in fig. 2c, a machine learning fracture identification result section plane view is shown along the top boundary of the Ordovician-type one-room group, and a machine learning fracture identification result section plane view is shown in fig. 2d, a machine learning fracture identification result section plane view is shown along the bottom boundary of the Midamask system. As shown in fig. 2 b-2 d, the machine learning technique has a good effect in application of sliding fracture and crack identification of the Tarim basin, the fracture and crack detection results are rich in details on the section and the plane, and the accuracy of characterization of the fracture zone and prediction of the crack reservoir is greatly improved.
S400, solving an amplitude curvature attribute of the three-dimensional seismic amplitude data volume to predict the hole type reservoir stratum.
The beneficial reservoirs of carbonate fracture-cave systems are generally divided into three types: fractured reservoir, caverned reservoir. In the envelope surface of the fracture zone, fracture development is closely related to fault and hole development, and areas with developed general fractures and holes are often accompanied by more dense fractures, so that fractured reservoirs generally show disordered reflection in earthquake and often show abnormal attributes related to fractures and holes in earthquake attributes. The forward modeling attribute simulation shows that the amplitude curvature has a good identification effect on the hole type reservoir, and the hole type reservoir can be effectively identified through the amplitude curvature attribute.
The amplitude curvature is derived from the seismic data amplitude by a second order transverse derivation. Firstly, the first derivative of the main survey line direction and the cross survey line direction is calculated by using the seismic amplitude or energy, and the obtained energy gradient attribute can reflect the abnormal geologic body, which is generally called amplitude energy gradient. And then carrying out second-order derivation on the amplitude curved surface to obtain an amplitude curved surface, and finally calculating each amplitude curvature attribute according to the surface fitting. In principle, the amplitude energy gradient is a manifestation of the edges of the geologic body, and therefore the spatial distribution of the geologic body cannot be obtained by the threshold value. The amplitude curvature converts the amplitude energy gradient into an attribute reflecting the geologic body envelope, and the geologic body spatial distribution can be obtained through a threshold value.
Fig. 3a is an overlay of a section of the identification effect of the S300 fracture and the amplitude curvature on the hole type reservoir stratum, and fig. 3b is an overlay of a plane of the identification effect of the S300 fracture and the amplitude curvature on the hole type reservoir stratum. The amplitude curvature is very sensitive to strong reflection of beads in terms of identification effect, holes and crack development zones around the holes can be well identified, the superposition display of the amplitude curvature and the crack identification result shows that the holes, the cracks and the crack development are closely related, the reservoir generally develops in the range of the crack zone, the amplitude curvature identification result shows that the longitudinal development difference of the fault-control reservoir body is large, the reservoir mainly develops in a shallow layer, and the deep reservoir develops gradually.
S500, well-seismic calibration is carried out on the three-dimensional seismic amplitude data volume, wavelets are extracted, a wave impedance data volume is obtained, residual wave impedance is further calculated, and the cave-type reservoir is identified by utilizing the residual wave impedance data volume.
The identification of the cave-type reservoir is a difficulty in the identification of the reservoir of a carbonate rock slotted hole system, and due to the limitation of seismic resolution, the position and the size of the cave-type reservoir are difficult to accurately position through conventional seismic attribute research, so that the identification of the cave-type reservoir needs to be carried out by adopting a high-resolution inversion means. The results of petrophysical research show that the impedance difference is still an effective distinguishing parameter between carbonate reservoirs and non-reservoirs, and the lower the impedance generally indicates that the reservoir physical properties are better. The cave-type reservoir is filled with mud and fluid and has obvious low impedance characteristics, so that the cave-type reservoir can be identified by adopting an inversion method.
According to the multilayer system reservoir body three-dimensional depiction and oil-gas spatial distribution and volume fixing method, preferably, the wave impedance data body is obtained through compressive sensing inversion in S500.
According to the method, a compressive sensing high-resolution inversion method is adopted for identifying the cavernous reservoir, a compressive sensing algorithm solves the sparse inversion problem through an L1 norm (conventional inversion uses an L2 norm), and the stratum reflection coefficient is assumed to be characterized through odd-even pole decomposition. The algorithm realizes that the precision and the resolution of an inversion result are improved by introducing the wavelet matrix into the deconvolution of the wedge-shaped dictionary. The objective function of the inversion is:
Min[||s-Wr||2+λ||r||p]
where s is seismic data, W is a wavelet, r is a reflection coefficient, λ is a regularization coefficient, and p is a norm. In compressed perceptual inversion p is 1.
The compressed sensing inversion uses a compressed sensing theory in the algorithm implementation process, and the obtained wave impedance is higher than the deterministic inversion resolution of the traditional commercial software; meanwhile, the noise immunity and stability are better, and the elastic parameters obtained by inversion are more accurate.
Designing forward models of a single cave and two adjacent caves which are longitudinally distributed, and carrying out inversion test; fig. 4a is a seismic model of linear fracture + single cave, fig. 4b is a seismic profile of linear fracture + single cave, fig. 4c is a wave impedance inversion profile of linear fracture + single cave, fig. 4d is a compressed sensing wave impedance inversion profile of linear fracture + single cave, fig. 4e is a seismic model of linear fracture + double cave, fig. 4f is a seismic profile of linear fracture + double cave, fig. 4g is a wave impedance inversion profile of linear fracture + double cave, and fig. 4h is a compressed sensing wave impedance inversion profile of linear fracture + double cave.
As can be seen from comparison of fig. 4a to 4d and fig. 4e to 4h, the wave impedance inversion has better recognition degree for the cavern type reservoir, and the compressed sensing inversion result has higher signal-to-noise ratio, obviously improved resolution, more focused cavern position, and can more accurately recognize the cavern position.
The transverse heterogeneity of the solution reservoir is very strong, the obtained wave impedance inversion section has certain continuity in the transverse direction, and the heterogeneity characteristic is not obvious enough, which is also determined by the characteristics of seismic data. In order to highlight the heterogeneity of a solution reservoir and eliminate the influence of transverse continuous seismic reflection, residual wave impedance processing is carried out on a wave impedance inversion result, and the processing method is that median filtering is carried out on the wave impedance inversion result to obtain a relatively smooth wave impedance data volume, and then the relatively smooth wave impedance data volume is subtracted from the original wave impedance data volume to obtain residual wave impedance.
FIG. 4i is a compressed sensing inversion residual impedance profile. From fig. 4i, through the compressed sensing high-resolution inversion processing, the resolution is obviously improved, the cavernous reservoir which is difficult to identify by the conventional attributes is clearly visible on the inversion section, and the emptying loss goodness of fit is high.
S600, combining the obtained identification and prediction results of the fracture, the hole type reservoir and the cave type reservoir, setting prediction threshold values of machine learning fracture and crack attributes, amplitude curvature attributes and inversion residual wave impedance attributes under the quality control and constraint of drilling and logging data, and achieving three-dimensional depiction of the multilayer system reservoir body, oil-gas spatial distribution and constant volume calculation.
The fracture-cavity carbonate rock aggregate has a complex internal structure and strong heterogeneity, cracks, holes and cavities are developed, and fracture-cavity development zones and cavity boundaries can be identified through multi-attribute fusion identification. In the process of attribute fusion, firstly, threshold values of different attributes are determined, and the data such as a drilling time curve, an emptying loss point and the like are mainly referred. Statistics show that the time-of-drilling curve is sensitive to the broken solution boundary and becomes faster when the broken solution is drilled, so that the time-of-drilling curve can be used to determine the threshold value of the attribute. When the drill encounters a fracture, a hole, a cave or a dense zone of cracks, loss, emptying and the like generally occur, so that threshold values of different attributes can be determined by utilizing the information.
Fig. 5a is a diagram of fusion effect of the properties of the broken solution, and the actual calibration result shows that the prediction result and the drilling calibration result have high correlation, the coincidence rate exceeds 80%, and the prediction result is relatively reliable.
Due to the fact that the porosity of reservoirs of different types has large difference, the porosity of reservoirs of different types needs to be classified and evaluated, effective volumes of reservoirs of different types are obtained, and therefore the trap resource amount is estimated.
The effective capacity calculation of fracture-cavity carbonate reservoirs is divided into three types, namely fractures, holes and caves, the physical property difference of different types of reservoirs is large, and the volume estimation needs to be carried out by adopting different methods.
The constant volume calculation comprises:
1) a fractured reservoir:
the development range of the fractured reservoir is wide, the fractured reservoir develops at the periphery of the cave reservoir and the hole reservoir, the relationship with the fault development is close, the dense area range of the fracture development can be predicted only approximately from the seismic attribute, and the fracture development area is predicted through machine learning. The porosity of the fractured reservoir is generally small, the porosity of the fractured reservoir is generally 0.2-2% as shown by well logging interpretation statistical results, the average value of 1.1% is taken as the porosity of the fractured reservoir, and the effective volume of the fractured reservoir can be calculated by combining machine learning fracture prediction attributesIn the example, the space distribution characteristics of a fractured-fluid internal fractured reservoir are shown in FIG. 5b, and the predicted effective volume is 0.61X 106m3。
2) A pore type reservoir layer:
the cavern type reservoir is generally associated with the vicinity of the cavern type reservoir, belongs to karst caves and karst holes with smaller scale relative to the cavern type reservoir, and is generally filled with mud or semi-mud, and the well drilling is generally carried out by emptying and leakage in a small amount. Due to the limitation of seismic resolution, the small-scale holes cannot be accurately identified from seismic inversion, so that the envelope surface of the reservoir is identified through amplitude curvature, and the total volume of space of the reservoir is determined; according to the statistics of logging data, the porosity distribution range of the reservoirs is small and is generally between 2% and 5%, in the reserve calculation, the average value is 3.5% to determine the porosity of the reservoirs, and further obtain the effective volume of the porous reservoirs, the spatial distribution characteristic of the porous reservoir in a certain solution breaking body is shown in fig. 5c, and the predicted effective volume is 20.54 × 106m3。
3) Cave-type reservoir bed:
the cavernous reservoir has the best physical property, but the filler is complex, the internal pore changes greatly, the numerical value change range can reach 5 to 100 percent, and the well drilling is generally emptying. According to the method, the compressed sensing high-resolution inversion result is used as the identification parameter of the cave type reservoir, so that a good cave boundary identification effect can be obtained. The statistics of the well logging shows that the porosity and the wave impedance have good correlation (fig. 5d), so that the inverse wave impedance can be used for well estimating the porosity of the cavernous reservoir, and the effective volume of the cavernous reservoir can be obtained through the porosity.
Fig. 5e 1-fig. 5e3 are diagrams of inversion wave impedance and estimated porosity profile of cavernous reservoir, where fig. 5e1 is a seismic profile, fig. 5e2 is an inversion wave impedance profile of cavernous reservoir, and fig. 5e3 is a porosity profile of cavernous reservoir, and it can be known from fig. 5e 1-fig. 5e3 that cavernous reservoir porosity can be estimated more accurately by compressive sensing of inversion wave impedance.
The spatial distribution characteristics of the cavernous reservoir in the solution of this example are shown in FIG. 5f, and the predicted effective volume is 0.87 × 106m3。
4) On the basis of calculating the effective volumes by classification, the effective volumes of the three types of reservoirs are added to obtain the actual effective volume of the reservoir, and the total effective volume in a certain solution breaker is 22.02 × 106m3. And the oil-gas saturation is combined to provide basis for the reserve volume or resource volume evaluation. According to the constant volume method, the single effective volume estimation can be carried out on the large-scale broken solution in the fracture zone to obtain the volume of the whole broken solution (figure 5g), different numbers in the figure 5g represent broken solution storage units with a certain scale, and the effective volume of each broken solution is correspondingly estimated.
The multilayer system reservoir three-dimensional depicting and oil-gas spatial distribution and constant volume method combines various seismic attributes, realizes accurate comprehensive depicting and constant volume calculation of the carbonate rock fracture-cave system under well control constraint, and practical project research verifies the reliability of the method, so that the method can effectively identify the three-dimensional spatial distribution and quantitative evaluation of the carbonate rock fracture-cave system, and has wide popularization value.
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention, and it will be obvious to those skilled in the art that other variations or modifications may be made on the basis of the above description, and all embodiments may not be exhaustive, and all obvious variations or modifications may be included within the scope of the present invention.
Claims (10)
1. A multilayer system reservoir body three-dimensional drawing and oil-gas space distribution and constant volume method is characterized by comprising the following steps:
s100, acquiring a three-dimensional seismic amplitude data volume and drilling logging data of a target area;
s200, conducting construction guiding filtering processing on the three-dimensional seismic amplitude data volume;
s300, identifying fractures and cracks through a machine learning technology on the basis of the three-dimensional seismic amplitude data volume;
s400, solving an amplitude curvature attribute of the three-dimensional seismic amplitude data volume to predict a hole type reservoir stratum;
s500, performing well-seismic calibration on the three-dimensional seismic amplitude data volume, extracting wavelets, solving a wave impedance data volume, further calculating residual wave impedance, and identifying a cavern type reservoir by using the residual wave impedance data volume;
s600, combining the obtained identification and prediction results of the fracture, the hole type reservoir and the cave type reservoir, setting prediction threshold values of machine learning fracture and crack attributes, amplitude curvature attributes and inversion residual wave impedance attributes under the quality control and constraint of drilling and logging data, and achieving three-dimensional depiction of the multilayer system reservoir body, oil-gas spatial distribution and constant volume calculation.
2. The method for three-dimensional characterization of a multilayer system reservoir and spatial distribution and volumetric measurement of hydrocarbons according to claim 1, wherein the step of performing structure-oriented filtering processing on the three-dimensional seismic amplitude data volume in S200 comprises:
s201, determining the azimuth of a reflection homophase axis through an earthquake inclination angle and an azimuth angle;
s202, calculating a coherent body, and determining the positions of main faults and cracks;
and S203, expanding filtering under the constraints of the inclination angle, the azimuth angle and the coherent body to finish the construction guiding filtering processing.
3. The method for multilayer system reservoir stereo depiction and oil and gas space distribution and constant volume as claimed in claim 2, wherein the filtering process specifically comprises: carrying out discontinuity detection according to the coherent body, and confirming a fracture development position; seismic data are not smoothed for identified fracture development locations, otherwise smoothed along the formation.
4. The method of claim 1, wherein the machine learning technique is a convolutional neural network machine learning technique.
5. The multilayer system reservoir stereographic and oil and gas spatial distribution and volumetric method according to claim 4, characterized in that the convolutional neural network machine learning technique specifically employs a fracture detection algorithm based on CNN image segmentation.
6. The method for multilayer system reservoir volume solid depiction and oil and gas space distribution and volumetric determination as claimed in claim 1, wherein the amplitude curvature attribute is obtained by performing a transverse second order derivation on seismic data amplitude; firstly, the first derivative of the direction of a main survey line and the direction of an interconnection survey line is calculated by utilizing seismic amplitude or energy to obtain an amplitude energy gradient, then, the second derivative is carried out to obtain an amplitude curved surface, and finally, the curvature attribute of each amplitude is calculated according to the surface fitting.
7. The method for three-dimensional characterization of multilayer system reservoirs and spatial distribution and volumetric determination of hydrocarbons according to claim 1, wherein the wave impedance data volume is obtained by compressive sensing inversion in S500.
8. The method for three-dimensional characterization of multilayer system reservoir and spatial distribution and volumetric measurement of hydrocarbons according to claim 7, wherein the calculation process of the residual wave impedance comprises: and carrying out median filtering on the wave impedance data volume to obtain a relatively smooth wave impedance data volume, and then subtracting the original wave impedance data volume to obtain the residual wave impedance.
9. The method of claim 1, wherein the predicted thresholds for different attributes are determined by reference to a drilling time curve and a venting loss point data.
10. The method of claim 9, wherein the volumetric calculation comprises:
1) a fractured reservoir: logging and explaining the porosity range of the reservoir, taking the average value of the porosity range as the porosity of the fractured reservoir, identifying fracture and fracture by combining a machine learning technology, and calculating the effective volume of the fractured reservoir;
2) a pore type reservoir layer: logging and explaining the porosity range of the reservoir, and taking the average value of the porosity range as the porosity of the pore type reservoir; identifying the envelope surface of the hole type reservoir through the amplitude curvature, and determining the total volume of the space of the reservoir so as to obtain the effective volume of the hole type reservoir;
3) cave-type reservoir bed: estimating the porosity of the cave-type reservoir by using a wave impedance data volume obtained by compressed sensing inversion, and obtaining the effective volume of the cave-type reservoir through the porosity;
4) and adding the effective volumes of the three types of reservoirs to obtain the effective volume of the multi-series reservoir.
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